Monitoring Maize Lodging Grades via Unmanned Aerial Vehicle Multispectral Image

Lodging is one of the main factors affecting the quality and yield of crops. Timely and accurate determination of crop lodging grade is of great significance for the quantitative and objective evaluation of yield losses. The purpose of this study was to analyze the monitoring ability of a multispectral image obtained by an unmanned aerial vehicle (UAV) for determination of the maize lodging grade. A multispectral Parrot Sequoia camera is specially designed for agricultural applications and provides new information that is useful in agricultural decision-making. Indeed, a near-infrared image which cannot be seen with the naked eye can be used to make a highly precise diagnosis of the vegetation condition. The images obtained constitute a highly effective tool for analyzing plant health. Maize samples with different lodging grades were obtained by visual interpretation, and the spectral reflectance, texture feature parameters, and vegetation indices of the training samples were extracted. Different feature transformations were performed, texture features and vegetation indices were combined, and various feature images were classified by maximum likelihood classification (MLC) to extract four lodging grades. Classification accuracy was evaluated using a confusion matrix based on the verification samples, and the features suitable for monitoring the maize lodging grade were screened. The results showed that compared with a multispectral image, the principal components, texture features, and combination of texture features and vegetation indices were improved by varying degrees. The overall accuracy of the combination of texture features and vegetation indices is 86.61%, and the Kappa coefficient is 0.8327, which is higher than that of other features. Therefore, the classification result based on the feature combinations of the UAV multispectral image is useful for monitoring of maize lodging grades.

[1]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[2]  J. A. Schell,et al.  Monitoring vegetation systems in the great plains with ERTS , 1973 .

[3]  S. U. Remison,et al.  Relationship between lodging, morphological characters and yield of varieties of maize (Zea mays L.) , 1978, The Journal of Agricultural Science.

[4]  H. A. Esechie,et al.  Relationship of stalk morphology and chemical composition to lodging resistance in maize (Zea mays L.) in a rainforest zone , 1985, The Journal of Agricultural Science.

[5]  A. Huete.  A soil-adjusted vegetation index (SAVI) , 1988 .

[6]  R. Waring,et al.  The normalized difference vegetation index of small Douglas-fir canopies with varying chlorophyll concentrations , 1994 .

[7]  J. Roujean,et al.  Estimating PAR absorbed by vegetation from bidirectional reflectance measurements , 1995 .

[8]  A. Gitelson,et al.  Use of a green channel in remote sensing of global vegetation from EOS- MODIS , 1996 .

[9]  E. V. Laureles,et al.  Lodging reduces yield of rice by self-shading and reductions in canopy photosynthesis , 1997 .

[10]  R. Tateishi,et al.  Relationships between percent vegetation cover and vegetation indices , 1998 .

[11]  G. Banko.  A Review of Assessing the Accuracy of Classifications of Remotely Sensed Data and of Methods Including Remote Sensing Data in Forest Inventory , 1998 .

[12]  William F. Tracy,et al.  Developmental changes associated with divergent selection for rind penetrometer resistance in the MoSCSSS maize Synthetic , 1999 .

[13]  Leen-Kiat Soh,et al.  Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices , 1999, IEEE Trans. Geosci. Remote. Sens..

[14]  D. Papakosta,et al.  Corn stalk traits related to lodging resistance in two soils of differing salinity. , 2000 .

[15]  Veerle Van Eetvelde,et al.  Holistic aspects of suburban landscapes: visual image interpretation and landscape metrics , 2000 .

[16]  Pi-Fuei Hsieh,et al.  Effect of spatial resolution on classification errors of pure and mixed pixels in remote sensing , 2001, IEEE Trans. Geosci. Remote. Sens..

[17]  S. Tarantola,et al.  Designing a spectral index to estimate vegetation water content from remote sensing data: Part 1 - Theoretical approach , 2002 .

[18]  A. Gitelson,et al.  Vegetation and soil lines in visible spectral space: A concept and technique for remote estimation of vegetation fraction , 2002 .

[19]  Wu Bingfang,et al.  Analysis to the relationship of classification accuracy, segmentation scale, image resolution , 2003, IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No.03CH37477).

[20]  Chenghai Yang,et al.  Comparison of QuickBird Satellite Imagery and Airborne Imagery for Mapping Grain Sorghum Yield Patterns , 2006, Precision Agriculture.

[21]  M. Gutierrez Rodriguez,et al.  Canopy reflectance, stomatal conductance, and yield of Phaseolus vulgaris L. and Phaseolus coccineus L. under saline field conditions , 2005 .

[22]  Zhao Chunjiang.  The Canopy Spectral Features and Remote Sensing of Wheat Lodging , 2005 .

[23]  Molly E. Brown,et al.  Evaluation of the consistency of long-term NDVI time series derived from AVHRR,SPOT-vegetation, SeaWiFS, MODIS, and Landsat ETM+ sensors , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[24]  Peijun Li,et al.  Multivariate Image Texture by Multivariate Variogram for Multispectral Image Classification , 2009 .

[25]  Mohd Nasir Taib,et al.  GLCM texture feature reduction for EEG spectrogram image using PCA , 2010, 2010 IEEE Student Conference on Research and Development (SCOReD).

[26]  M. Tomosada,et al.  Novel Mixture Model for Mixed Pixel Classification of Multispectral Image Data , 2010 .

[27]  Thomas Blaschke,et al.  Land cover change assessment using decision trees, support vector machines and maximum likelihood classification algorithms , 2010, Int. J. Appl. Earth Obs. Geoinformation.

[28]  Wenjiang Huang,et al.  Comparison of Spectral Indices and Principal Component Analysis for Differentiating Lodged Rice Crop from Normal Ones , 2011, CCTA.

[29]  Jing He,et al.  Research of UAV Flight Planning Parameters , 2012 .

[30]  John Spink,et al.  Predicting yield losses caused by lodging in wheat , 2012 .

[31]  H. Feidas,et al.  Classification of convective and stratiform rain based on the spectral and textural features of Meteosat Second Generation infrared data , 2013, Theoretical and Applied Climatology.

[32]  S. Quegan,et al.  Analysis of Maximum Likelihood classification technique on Landsat 5 TM satellite data of tropical land covers , 2012, 2012 IEEE International Conference on Control System, Computing and Engineering.

[34]  Asmala Ahmad.  Classification Simulation of RazakSAT Satellite , 2013 .

[35]  P. Sathyanarayana,et al.  Image Texture Feature Extraction Using GLCM Approach , 2013 .

[36]  Helmi Zulhaidi Mohd Shafri,et al.  Image Classification in Remote Sensing , 2013 .

[37]  Kanika Kalra,et al.  A COMPARATIVE STUDY OF SUPERVISED IMAGE CLASSIFICATION ALGORITHMS FOR SATELLITE IMAGES , 2013 .

[38]  Karen O. Egiazarian,et al.  Face recognition using scale-adaptive directional and textural features , 2014, Pattern Recognit..

[39]  Hong Chun Zhu,et al.  The Extraction of Wheat Lodging Area in UAV’s Image Used Spectral and Texture Features , 2014 .

[40]  J. Kovacs,et al.  Applications of Low Altitude Remote Sensing in Agriculture upon Farmers' Requests– A Case Study in Northeastern Ontario, Canada , 2014, PloS one.

[41]  Weiqi Zhou,et al.  Quantifying Spatial Heterogeneity in Urban Landscapes: Integrating Visual Interpretation and Object-Based Classification , 2014, Remote. Sens..

[42]  G. Rowland,et al.  Genomic regions underlying agronomic traits in linseed (Linum usitatissimum L.) as revealed by association mapping , 2014, Journal of integrative plant biology.

[43]  Gerald Schaefer,et al.  Fusion of multi-spectral and panchromatic satellite images using principal component analysis and fuzzy logic , 2014, 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).

[44]  Marco Dubbini,et al.  Evaluating Multispectral Images and Vegetation Indices for Precision Farming Applications from UAV Images , 2015, Remote. Sens..

[45]  Arthur P. Cracknell,et al.  Satellite Images for Monitoring Mangrove Cover Changes in a Fast Growing Economic Region in Southern Peninsular Malaysia , 2015, Remote. Sens..

[46]  J. Alex Thomasson,et al.  Comparison of mosaicking techniques for airborne images from consumer-grade cameras , 2016 .

[47]  Giovanna Jona Lasinio,et al.  A low-cost drone based application for identifying and mapping of coastal fish nursery grounds , 2016 .

[48]  Dharun V.S,et al.  Extraction of Texture Features using GLCM and Shape Features using Connected Regions , 2016 .

[49]  Atique Ur Rehman,et al.  Lodging stress in cereal—effects and management: an overview , 2017, Environmental Science and Pollution Research.

[50]  F. Maupas,et al.  Retrieving LAI, chlorophyll and nitrogen contents in sugar beet crops from multi-angular optical remote sensing: Comparison of vegetation indices and PROSAIL inversion for field phenotyping , 2017 .

[51]  P. M. Berry,et al.  Quantifying lodging-induced yield losses in oilseed rape , 2017 .

[52]  Ali Noman,et al.  Alteration in yield and oil quality traits of winter rapeseed by lodging at different planting density and nitrogen rates , 2018, Scientific Reports.

[53]  Douglas D. Cook,et al.  Maize Stalk Lodging: Morphological Determinants of Stalk Strength , 2017 .

[54]  Ming-Der Yang,et al.  Spatial and Spectral Hybrid Image Classification for Rice Lodging Assessment through UAV Imagery , 2017, Remote. Sens..

[55]  Seth C. Murray,et al.  Assessing Lodging Severity over an Experimental Maize (Zea mays L.) Field Using UAS Images , 2017, Remote. Sens..

[56]  Blake M. Allan,et al.  Applications of unmanned aerial vehicles in intertidal reef monitoring , 2017, Scientific Reports.

[57]  Baofeng Su,et al.  Significant Remote Sensing Vegetation Indices: A Review of Developments and Applications , 2017, J. Sensors.

[58]  Hao Yang,et al.  Quantitative Identification of Maize Lodging-Causing Feature Factors Using Unmanned Aerial Vehicle Images and a Nomogram Computation , 2018, Remote. Sens..

[59]  Min Jiang,et al.  Estimates of rice lodging using indices derived from UAV visible and thermal infrared images , 2018 .

[60]  K. M. Idris,et al.  Comprehensive analysis of flying altitude for high resolution slope mapping using UAV technology , 2018 .

[61]  Guijun Yang,et al.  Clustering Field-Based Maize Phenotyping of Plant-Height Growth and Canopy Spectral Dynamics Using a UAV Remote-Sensing Approach , 2018, Front. Plant Sci..

[62]  Enrique V. Carrera,et al.  A Finger-vein Biometric System Based on Textural Features , 2018, ICITS.

[63]  Mirco Boschetti,et al.  Remote sensing-based crop lodging assessment: Current status and perspectives , 2019, ISPRS Journal of Photogrammetry and Remote Sensing.

[64]  Andreas Burkart,et al.  Quantifying Lodging Percentage and Lodging Severity Using a UAV-Based Canopy Height Model Combined with an Objective Threshold Approach , 2019, Remote. Sens..

[65]  J. Poland,et al.  High-Throughput Phenotyping Enabled Genetic Dissection of Crop Lodging in Wheat , 2019, Front. Plant Sci..